GEP5 model for multiple myeloma

ABSTRACT

The invention provides, inter alia, methods of prognosing a subject with, or suspected of having, multiple myeloma. In certain embodiments, the methods entail testing the gene expression levels of enolase 1 (ENO1), fatty acid binding protein 5 (FABP5), thyroid hormone receptor interactor 13 (TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1) 4 (RFC4) in a biological sample isolated from the subject. The invention also provides methods of treatment for multiple myeloma, as well as kits, oligonucleotides, and systems for performing the methods provided by the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. application Ser. No.14/892,555, which is the U.S. National Stage of PCT/US2014/038626, filedMay 19, 2014, which claims the benefit of U.S. Provisional ApplicationNo. 61/825,396, filed on May 20, 2013. The entire teachings of the aboveapplication are incorporated herein by reference.

The instant application contains a Sequence Listing which has beensubmitted in ASCII format via EFS-WEB and is hereby incorporated byreference in its entirety. Said ASCII copy, created on Nov. 16, 2017, isnamed sequence.txt and is 3 KB.

GOVERNMENT SUPPORT

This invention was made with government support under P01CA055819awarded by the National Institutes of Health. The government has certainrights in the invention.

BACKGROUND OF THE INVENTION

Multiple myeloma is the second most common hematological malignancy inthe U.S., constituting about 1% of all diagnosed cancers. Multiplemyeloma develops in about 1-4 per 100,000 people per year. Survivaltimes amongst patients are varied—with conventional treatment, mediansurvival is 3-4 years, which may be extended, in some patients, to 5-7years or longer with advanced treatments. Patients are not uniform intheir need for certain advanced therapies, however, so there areadditional burdens on all affected parties when advanced treatments areimproperly applied or withheld.

Given the immense personal and financial burden of multiple myeloma onpatients, their social networks, and the healthcare system, and thevaried survival times and responses to treatments, a need exists formethods for the prognosis of subjects that have, or are suspected ofhaving, multiple myeloma. Preferably, such methods should be capable ofstratifying subjects based on genetic information, particularly whenlimited amounts of genetic material are available for the prognosis.

SUMMARY OF THE INVENTION

The invention provides methods of prognosing subjects that have, or aresuspected of having, multiple myeloma. Advantageously, the methodsprovided by the invention are capable of prognosing subjects using alimited amount of genetic material. The invention is based, at least inpart, on Applicant's discovery of a small set of (five, or even as fewas two) informative genes whose expression levels can be used tostratify subjects with multiple myeloma—in some embodiments using abiological sample with a limited number of cells.

In a first aspect, the invention provides methods of prognosing asubject suspected of having multiple myeloma. The methods include thesteps of: testing the gene expression level of enolase 1 (ENO1), fattyacid binding protein 5 (FABP5), thyroid hormone receptor interactor 13(TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1)4 (RFC4) in a biological sample isolated from the subject using a methodcapable of detecting the expression level of the genes when thebiological sample comprises about 50,000 or fewer myeloma cells; andquantifying the gene expression levels of each of ENO1, FABP5, TRIP13,TAGLN2, and RFC4, where an abnormal gene expression profile as comparedto a suitable control is associated with a poor prognosis for thesubject. In some embodiments, the poor prognosis is reduced likelihoodof overall survival (OS) and/or reduced likelihood of progression-freesurvival (PFS).

In certain embodiments, the methods provided by the invention include astep of assigning the subject a prognosis on the basis of the testedgene expression levels, e.g., on the basis of a presence or absence ofan abnormal gene expression profile.

In some embodiments, the gene expression levels are tested at theprotein level.

In other embodiments, the gene expression levels are tested at thenucleic acid level. In more particular embodiments, the gene expressionlevels are tested by quantitative polymerase chain reaction (qPCR),quantitative real-time polymerase chain reaction (qRTPCR), digitaldroplet PCR, (ddPCR), sequencing, northern blotting, or Southernblotting. In more particular embodiments, the gene expression levels aretested by qRTPCR. In still more particular embodiments, the geneexpression levels are tested by qRTPCR comprising the use of sets ofthree primers for each of the genes, wherein for each set of threeprimers at least one of the primers is detectably labeled and is subjectto polymerase-dependent hydrolysis in the presence of the targettemplate of the set of three primers—e.g., TAQMAN®. In still moreparticular embodiments, the detectable label is a fluorescent label. Inother particular embodiments, the gene expression levels are tested byqRTPCR using primers selected from Hs00361415_m1, Hs02339439_g1,Hs00188500_m1, Hs00761239_s1, Hs00427469 m1, primers listed in Table B,or primers substantially similar to those listed in Table B.

In some embodiments, the subject is undergoing myeloma therapy.

In particular embodiments, the method is capable of prognosing OS in asubject undergoing TT2.

In other particular embodiments, the method is capable of prognosingboth OS and PFS in a subject undergoing TT3a.

In some embodiments, the method is capable of prognosing PFS in asubject undergoing TT3b.

In other embodiments, the method is capable of prognosing PFS in asubject undergoing TT4 or TT5. In more particular embodiments, themethod is capable of prognosing both OS and PFS in a subject undergoingTT4 or TT5.

In some embodiments, the method is capable of prognosing OS in a subjectundergoing TT6.

In another aspect, the invention provides method of prognosingprogression free survival (PFS) in a subject with multiple myelomaundergoing Total Therapy 3b (TT3b). These methods include the steps of:testing the gene expression level of enolase 1 (ENO1), fatty acidbinding protein 5 (FABP5), thyroid hormone receptor interactor 13(TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1)4 (RFC4) in a biological sample isolated from the subject; andquantifying the gene expression levels of each of ENO1, FABP5, TRIP13,TAGLN2, and RFC4, where an abnormal gene expression profile isassociated with a reduced likelihood of PFS for the subject.

In another aspect, the invention provides methods of prognosingprogression free survival (PFS) or overall survival (OS) in a subjectwith multiple myeloma undergoing Total Therapy 4 (TT4) or Total Therapy5 (TT5) by testing the gene expression level of enolase 1 (ENO1), fattyacid binding protein 5 (FABP5), thyroid hormone receptor interactor 13(TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1)4 (RFC4) in a biological sample isolated from the subject andquantifying the gene expression levels of each of ENO1, FABP5, TRIP13,TAGLN2, and RFC4, where an abnormal gene expression profile isassociated with a reduced likelihood of PFS or OS for the subject. Inparticular embodiments, the method is for prognosing OS. In otherparticular embodiments, an abnormal gene expression profile is anindependent averse factor for PFS as evaluated by stepwise regression.

In some embodiments of any of the preceding aspects and embodiments, thesubject is a human.

In particular embodiments of any of the preceding aspects orembodiments, the subject exhibits an abnormal gene expression profileand is thereby determined to have a poor prognosis and is changed tomore frequent surveillance schedule.

In particular embodiments of any of the preceding aspects orembodiments, the subject does not exhibit an abnormal gene expressionprofile and is thereby determined to have a favorable prognosis.

In particular embodiments of any of the preceding aspects orembodiments, the subject is undergoing treatment with a proteasomeinhibitor, an immunomodulatory drug, cisplatin, etoposide,cyclophosphamide, melphalan, cellular therapy with expanded NK cells,cellular therapy with T-cells, antibody therapy, dexamethasone, orcombinations thereof.

In particular embodiments of any of the preceding aspects orembodiments, the gene expression levels are log-normalized.

In some embodiments of any of the preceding aspects or embodiments, thedisease index is calculated as the mean of log-normalized geneexpression levels of the genes.

In particular embodiments of any of the preceding aspects orembodiments, the method discriminates between poor and favorableprognosis for OS or PFS with a hazard ratio of at least 1.5, 1.6, 1.7,1.8, 1.9, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2,4.4, 4.6, 4.8, 5.0, 5.2, 5.4, 5.6, 5.8, or 6.0.

In some embodiments of any of the preceding aspects or embodiments, thebiological sample comprises fewer than 45,000; 40,000; 30,000; 20,000;10,000; 9,000; 8,000; 7,000; 6,000; 5,000; 4,000; 3,000; 2,000; 1,000;900; 800; 700; 600; 500; 400; 300; 200; 100; 90; 80; 70; 60; 50; 40; 30;20; 10; 9; 8; 7; 6; 5; 4; 3; or 2 myeloma cells. In particularembodiments, the myeloma cells are selected by CD138⁺ expression,CD38⁺/CD45^(dim) expression, or CD38⁺/CD45^(neg) expression.

In another aspect, the invention provides methods of prognosing asubject suspected of having multiple myeloma by testing, by qRT-PCR, thenucleic acid gene expression level of enolase 1 (ENO1), fatty acidbinding protein 5 (FABP5), thyroid hormone receptor interactor 13(TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1)4 (RFC4) in a nucleic acid sample isolated from a biological sampleisolated from the subject and calculating the mean, log-normalized geneexpression level of ENO1, FABP5, TRIP13, TAGLN2, and RFC4, where anelevated mean, log-normalized gene expression level of ENO1, FABP5,TRIP13, TAGLN2, and RFC4, relative to a suitable control, is associatedwith a poor prognosis for the subject.

In yet another aspect, the invention provides methods of treatingmultiple myeloma in a subject, comprising administering a suitabletherapy to the subject on the basis of a prognosis by the method of anyone of the preceding aspects and embodiments.

In another aspect, the invention provides kits containing reagents forperforming the method of any of the preceding aspects and embodiments,optionally including suitable positive and/or negative controls. In moreparticular embodiments, the kit includes one or more of any one of theprimers Hs00361415_m1, Hs02339439_g1, Hs00188500_m1, Hs00761239_s1,Hs00427469_m1, those listed in Table B, or primers substantially similarto those listed in Table B.

In yet another aspect, the invention provides a non-transitorycomputer-readable storage medium that provides instructions that, ifexecuted by a processor, causes the processor to perform operationsincluding: reading data representing the gene expression level ofenolase 1 (ENO1), fatty acid binding protein 5 (FABP5), thyroid hormonereceptor interactor 13 (TRIP13), transgelin 2 (TAGLN2), and replicationfactor C (activator 1) 4 (RFC4) determined for an isolated biologicalsample obtained from a subject; analyzing the data to determine thepresence of an abnormal gene expression profile as compared to asuitable control; and providing a classification of the subject on thebasis of the data analysis, wherein the presence of an abnormal geneexpression profile is associated with a poor prognosis for the subject.In some embodiments, the instructions further comprise a step ofproviding a therapeutic recommendation on the basis of the dataanalysis.

In another aspect, the invention provides a system comprising thestorage medium of the preceding aspect and a processor.

In another aspect, the invention provides methods of any one of thepreceding aspects and embodiments where the presence of an abnormal geneexpression profile is determined using a storage medium or systemprovided by the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIGS. 1A-1D are Kaplan-Meier plots illustrating that GEP5 distinguisheda high- and a low-risk group with significantly different overall andprogression free survival in the TT6 and TT3A training sets as well asthe TT3B and TT2 validation sets. FIG. 1A shows the TT6 training setresults. GEP 5 identified a high-risk subset with a 1-year estimate OSof 60% (left panel) and PFS of 50% (right panel), compared to 95% and91%, respectively, for GEP5 low-risk. FIG. 1B shows the TT3A trainingset results. GEP 5 identified a high-risk subset with a 5-year estimateOS of 38% (left panel) and PFS of 33% (right panel), compared to 81% and71%, respectively, for GEP5 low-risk. FIG. 1C shows the TT3B validationset results. GEP 5 identified a high-risk subset with a 5-year estimateOS of 45% (left panel) and PFS of 31% (right panel), compared to 80% and74%, respectively, for GEP5 low-risk. FIG. 1D shows the TT2 validationset results. GEP 5 identified a high-risk subset with a 5-year estimateOS of 40% (left panel) and PFS of 26% (right panel), compared to 71% and49%, respectively, for GEP5 low-risk.

FIGS. 2A-2D are Kaplan-Meier plots illustrating that GEP70 distinguisheda high- and a low-risk group with significantly different overall andprogression free survival in the TT6 and TT3A training sets as well asthe TT3B and TT2 validation sets using the published cut-off of 0.66.FIG. 2A shows the TT6 training set results. 2-year estimate for OS of18% (left panel) and PFS of 19% (right panel) for GEP70 high-risk,compared to 95% and 72%, respectively, for GEP70 low-risk. FIG. 2B showsthe TT3A training set results. 5-year estimate for OS of 35% (leftpanel) and PFS of 25% (right panel) for GEP70 high-risk, compared to 80%and 72%, respectively, for GEP7 low-risk. FIG. 3C shows the TT3Bvalidation set results. 5-year estimate for OS of 38% (left panel) andPFS of 35% (right panel) for GEP70 high-risk, compared to 80% and 70%,respectively, for GEP70 low-risk. FIG. 2D shows the TT2 validation setresults. 5-year estimate OS of 28% (left panel) and PFS of 15% (rightpanel) for GEP70 high-risk, compared to 72% and 50%, respectively, forGEP70 low-risk.

FIG. 3 provides Kaplan-Meier plots illustrating that a score from as fewas 2 probes (top 2 probes in TT6 training set) differentiated betweenhigh-risk and low-risk myeloma in TT6. OS—left panel; PFS—right panel.

FIGS. 4A-4D are Kaplan-Meier plots comparing performances of the GEP5and GEP70 model by treatment arm of TT2. FIGS. 4A-4B show the TT2thalidomide arm (thal+) results. GEP5 (upper panels; FIG. 4A) identifieda high- and a low-risk group in the TT2+thal arm for OS (left panels)and PFS (right panels). GEP70 (lower panels; FIG. 4B), which wasdeveloped on TT2, showed a slightly better performance for OS and PFS.FIGS. 4C-4D show the TT2 non-thalidomide arm (thal−) results. GEP5(upper panels; FIG. 4C) could identify a high- and a low-risk group inthe TT2−thal arm for OS (left panels) and PFS (right panels). GEP70(lower panels; FIG. 4D), which was developed on TT2, showed a slightlybetter performance for OS and PFS.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

Prognostic Methods

The invention provides, inter alia, prognostic methods—and kits andreagents for performing the methods—for subjects with, or suspected ofhaving, multiple myeloma. The methods are based on gene expressionprofiling using (comprising, consisting essentially of, or consistingof) the genes listed in Table A, including, in some embodiments, anysubset thereof, e.g., using 2 (e.g., ENO1 and FABP5), 3 (e.g., ENO1,FABP5, and TRIP13), 4 (e.g., ENO1, FABP5, TRIP13, and TAGLN2), or all 5genes. In a preferred embodiment, all five of ENO1, FABP5, TRIP13,TAGLN2, and RFC4 are used. The genes listed in Table A are humanreference sequences, and homologues from other mammalian species areknown in the art and may be easily obtained from reference databases(such as the NCBI Entrez portal) using, inter alia, the information inTable A.

TABLE A Gene Symbol GeneID RefRNA RefProtein ENO1 2023 NM_001201483.1NP_001188412.1 NM_001428.3 NP_001419.1 FABP5 2171 NM_001444.2NP_001435.1 TRIP13 9319 NM_001166260.1 NP_001159732.1 NM_004237.3NP_004228.1 TAGLN2 8407 NM_003564.1 NP_003555.1 RFC4 5984 NM_002916.3NP_002907.1 NM_181573.2 NP_853551.1In some embodiments, the gene expression profiling of genes comprisingthose in Table A, as well as subsets thereof, omits at least one (e.g.,1, 2, 3, 4, 5, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or all56) of: GNG10, PNPLA4, KIAA1754, AHCYL1, MCLC, EVI5, AD-020, PARG1,CTBS, FUCA1, RFP2, FLJ20489, LTBP1, AIM2, SELI, SLC19A1, LARS2, OPN3,ASPM, CCT2, UBE21, STK6, FLJ13052, FLJ12525, BIRC5, CKS1B, CKAPl,MGC57827, DKFZp7790175, PFN1, ILF3, IFI16, TBRG4, PAPDl, EIF2C2,MGC4308, DSG2, EXOSC4, RUVBL1, ALDOA, CPSF3, MGC15606, LGALS1, RAD18,SNX5, PSMD4, RAN, KIF14, CBX3, TMPO, DKFZP586L0724, WEEl, ROBO1, TCOF1,YWHAZ, and MPHOSPH1. In certain embodiments, the gene expressionprofiling of genes comprising those in Table A, as well as subsetsthereof, omits at least one (e.g., 1, 2, 3, 4, 5, 7, 8, 9, 10, 15, 20,25, 30, 35, 40, 45, 50, 55, 60, or all 65) of a gene detectable with oneof the following AFFYMETRIX® AffyIDs: 1555864_s_at, 206513_at,1555274_a_at, 211576_s_at, 204016_at, 1565951_s_at, 219918_s_at,201947_s_at, 213535_s_at, 204092_s_at, 213607_x_at, 208117_s_at,210334_x_at, 201897_s_at, 216194_s_at, 225834_at, 238952_x_at,200634_at, 208931_s_at, 206332_s_at, 220789_s_at, 218947_s_at,213310_at, 224523_s_at, 217901_at, 226936_at, 58696_at, 201614_s_at,200966_x_at, 225082_at, 242488_at, 243011_at, 201105_at, 224200_s_at,222417_s_at, 210460_s_at, 200750_s_at, 206364_at, 201091_s_at,203432_at, 221970_s_at, 212533_at, 213194_at, 244686_at, 200638_s_at,205235_s_at, 201921_at, 227278_at, 209740_s_at, 227547_at, 225582_at,200850_s_at, 213628_at, 209717_at, 222495_at, 1557277_a_at, 1554736_at,218924_s_at, 226954_at, 202838_at, 230192_at, 48106_at, 237964_at,202729_s_at, and 212435 at.

“Prognosing” is assigning a risk stratification to a subject for one ofat least two different risk groups for a clinical indicator. Inparticular embodiments, the at least two risk groups comprise poorprognosis and favorable prognosis for a clinical indicator. Exemplaryclinical indicators include two year overall survival (OS) and two yearprogression free survival (PFS).

A “subject” is a mammal, including primates (e.g., humans or monkeys),cows, sheep, goats, horses, dogs, cats, rabbits, guinea pigs, rats, andmice, or other bovine, ovine, equine, canine, feline, rodent or murinespecies. Examples of suitable subjects include, but are not limited to,human patients (e.g., a human with, or suspected of having, multiplemyeloma). The subject can be at any stage of development, includingprenatal, perinatal, infant, toddler, child, young adult, adult,middle-aged, or geriatric. In more particular embodiments, the subjectis a young adult, adult, middle-aged, or geriatric. In some embodiments,the subject is undergoing a myeloma treatment as defined below. Incertain embodiments, a myeloma treatment may be indicated (or modified)based on the results of the methods provided by the invention. Where asubject prognosed by a method provided by the invention is “undergoing”any therapy, such as a myeloma therapy, such as TT1, TT2, TT3, TT4, TT5,or TT6, the biological sample can be obtained before, during, after, ora combination thereof (i.e., 1, 2, or all 3 of before, during, or afterthe treatment).

“Multiple myeloma” includes subjects with symptomatic myeloma,asymptomatic myeloma, and monoclonal gammopathy of undeterminedsignificance (MGUS), as defined in Kyle and Rajkumar, Leukemia 23:3-9(2009, PubMedID 18971951, incorporated by reference in its entirety), aswell as the other stratifications and stages described in Kyle andRajkumar 2009. In particular embodiments, “multiple myeloma” issymptomatic myeloma.

“Gene expression” refers to both nucleic acid level (e.g., mRNA or cDNAderived from it) and protein level expression of a gene. Genes expressedas nucleic acids may or may not encode for and/or be translated intoprotein. The physical product of gene expression is a “gene expressionproduct.”

“Level of expression,” “expression level,” “gene expression level” andthe like are the amount of a gene expression product (e.g., nucleic acidor protein). Expression levels may be transformed (e.g., normalized) oranalyzed “raw.”

“Expression profile” or “gene expression profile” means at least twogene expression levels. For example, in some embodiments, the two ormore gene expression levels may be the gene expression level of one geneat two or more time points or the expression levels of two or moredifferent genes at the same, or different, times. For example, inparticular embodiments, an expression profile is the expression level ofENO1, FABP5, TRIP13, TAGLN2, and RFC4 at one or more time points.

“Abnormal gene expression profile” refers to a significant statisticaland/or practical deviation in the expression level of one or more genes,relative to a suitable control. “Suitable controls” include, forexample, paired samples from a single patient (e.g., biological samplesobtained at different times from a patient, e.g., before and afterdeveloping multiple myeloma) as well as reference values (such as anensemble of reference values representing ranges associated with aparticular prognosis) previously compiled from samples determined—by anymeans—to have a particular prognosis (e.g., poor or favorableprognosis). For example, reference values for one or more genes (e.g.,all five of ENO1, FABP5, TRIP13, TAGLN2, and RFC4) may be compiled andused to develop a binary or probabilistic classification algorithm thatis then used to classify a patient as having favorable or poorprognosis. Alternatively, a given expression profile (from the subject)is evaluated by clustering or otherwise assigning the gene expressionprofile to an existing group with a favorable prognosis or an existinggroup with an unfavorable or poor prognosis. In the present invention,an elevated level of ENO1, FABP5, TRIP13, TAGLN2, or RFC4, relative to asuitable control, is associated with a poor prognosis for a subject,and, therefore, in particular embodiments, an abnormal gene expressionprofile comprises elevated levels of 1, 2, 3, 4, or all 5 of ENO1,FABP5, TRIP13, TAGLN2, and RFC4, relative to a suitable control. Forexample, in particular embodiments, the presence of an abnormal geneexpression profile is determined by calculating the mean oflog-normalized (e.g., log₂) gene expression levels of ENO1, FABP5,TRIP13, TAGLN2, and RFC4, wherein a mean above a predetermined threshold(e.g., at least or about 10.68) indicates a poor prognosis.

“Testing” a gene expression level entails contacting a biological samplewith one or more isolated reagents for detecting a gene expressionlevel—i.e., the gene expression levels of ENO1, FABP5, TRIP13, TAGLN2,and RFC4—to measure the amount of a gene expression product by ananalytical laboratory method. Testing a level of a gene expressionproduct may be done directly in the course of the analytical laboratorymethod or, in some embodiments, by evaluating the quantitative output ofthe analytical laboratory methods.

“Isolated reagents for detecting a gene expression level” are isolatedanalytical reagents in substantially purified form adapted for use indetecting gene expression levels of a target analyte and include, forexample, isolated oligonucleotides complementary to a nucleic acidtarget analyte or, in some embodiments, antibodies that specificallybind a protein target analyte. In some embodiments, isolated reagentsfor detecting a gene expression level are products of man that aremarkedly different from compounds that exist in nature. In particularembodiments, the isolated reagents for detecting a gene expression levelare artificially and detectably labeled. In some embodiments, thebiological sample is transformed into something markedly different inorder to detect gene expression levels—i.e., the biological sample isartificially and detectably labeled, either directly, or, in someembodiments, by complexing the biological sample with isolated reagentsfor detecting a gene expression level that are, e.g., artificially anddetectably labeled.

“Target analyte” is all or part of a gene expression product, eitherprotein or nucleic acid, that is sufficient to identify the analyte fromother compounds that might be present in a sample and, as used in thisapplication, comprises all or part of a gene expression product forENO1, FABP5, TRIP13, TAGLN2, or RFC4.

“Target template” refers to a nucleic acid target analyte.

Any biological sample containing cells from the subject can be used inthe methods provided by the invention. In certain embodiments, thebiological sample comprises fewer than about 50,000 cells, e.g., fewerthan 45,000; 40,000; 30,000; 20,000; 10,000; 9,000; 8,000; 7,000; 6,000;5,000; 4,000; 3,000; 2,000; 1,000; 900; 800; 700; 600; 500; 400; 300;200; 100; 90; 80; 70; 60; 50; 40; 30; 20; 10; 9; 8; 7; 6; 5; 4; 3; or 2myeloma cells. In particular embodiments, the myeloma cells are selected(e.g., by flow cytometry or laser capture microscopy) by CD138⁺expression, CD38⁺/CD45^(dim) expression, or CD38⁺/CD45^(neg) expression.

Detection Methods and Kits

To obtain a gene expression profile, two or more gene expression levelsare determined. Expression levels can be determined by measuring and/ortesting at the nucleic acid or protein level, or a combination thereof,as well as, in some embodiments, analyzing previously-determined levels.Any means of determining gene expression levels can be employed whenpracticing the methods provided by the invention. In particularembodiments, the sensitivity of the method used is such that the geneexpression levels can be detected in a sample containing fewer thanabout 50,000 myeloma cells.

For example, levels of nucleic acid gene expression products can bedetermined in a number of ways, including polymerase chain reaction(PCR), including reverse transcriptase (rt) PCR, droplet digital PCR,real-time and quantitative PCR methods (including, e.g., TAQMAN®,molecular beacon, LIGHTUP™, SCORPION™, SIMPLEPROBES®; see, e.g., U.S.Pat. Nos. 5,538,848; 5,925,517; 6,174,670; 6,329,144; 6,326,145 and6,635,427); Northern blotting; Southern blotting of reversetranscription products and derivatives; array based methods, includingblotted arrays or in situ-synthesized arrays; and sequencing, e.g.,sequencing by synthesis, pyrosequencing, dideoxy sequencing, orsequencing by ligation, or any other methods known in the art, such asdiscussed in Shendure et al., Nat. Rev. Genet. 5:335-44 (2004) orNowrousian, Euk. Cell 9(9):1300-1310 (2010), including such specificplatforms as HELICOS®, ROCHE® 454, ILLUMINA®/SOLEXA®, ABI SOLiD®, andPOLONATOR® sequencing. In particular embodiments, the levels of nucleicacid gene expression products are measured by qRT-PCR. In still moreparticular embodiments, the qRT-PCR uses three nucleic acid sets foreach gene, where the three nucleic acids comprise a primer pair togetherwith a probe that binds between the regions of a target nucleic acidwhere the primers bind—known commercially as a TAQMAN® assay. Inparticular embodiments, primers for use in the methods provided by theinvention include Hs00361415_m1, Hs02339439_g1, Hs00188500_m1,Hs00761239_s1, and Hs00427469_m1, as well as those provided in Table Band probes substantially similar to those in Table B. For example, insome embodiments, the pairs labeled “primer” for a particular gene inTable B are used. In more particular embodiments, the accompanying“probe” for the particular gene in Table B is used as well, and, in moreparticular embodiments, the probe includes a detectable label, such as afluorescent label. “Substantially similar” probes can functionallysubstitute for those in Table B in the methods provided by theinvention. In some embodiments, substantially similar sequences are 60,65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 99.5, or 100% identical tothe sequences in Table B; or, alternatively or additionally,substantially similar sequences have endpoints within 100, 90, 80, 70,60, 50, 40, 35, 30, 25, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9,8, 7, 6, 5, 4, 3, 2, 1, or 0 nucleotides upstream or downstream ofeither of the endpoints of the those in Table B.

TABLE B SEQ ID Gene SequenceName Sequence  NO: Product ENO1 Probe ENO1AGAAGCCAAGCTC SEQ ID  Probe CCTGGAG NO: 1 ENO1 Primer ENO1 GTACCGCTTCCTTSEQ ID  Primer sense AGAAC NO: 2 ENO1 Primer ENO1 CTCACATGACTCT SEQ ID Primer anti-sense AGACAC NO: 3 FABP5 Probe FABP5 CCACTCCTGATGC SEQ ID Probe TGAACCA NO: 4 FABP5 Primer FABP5 GACTGTCTGCAAC SEQ ID  Primersense TTTAC NO: 5 FABP5 Primer FABP5 CCATCTTTCAATT SEQ ID  Primeranti-sense TTCTTGTTA NO: 6 TRIP13 Probe TRIP13 TCTTCTGGCTTCT SEQ ID Probe ATAACACCTGC NO: 7 TRIP13 Primer TRIP13 GCCAGCAAGTTTT SEQ ID Primer sense GTTTA NO: 8 TRIP13 Primer TRIP13 GCTTCTTTAGGGT SEQ ID Primer anti-sense GACAC NO: 9 TAGLN Probe TAGLN TGATGCTGCCTCT SEQ ID Probe GCCTTCT NO: 10 TAGLN Primer TAGLN TCCTCCGTTCATT SEQ ID  Primersense CCATG NO: 11 TAGLN Primer TAGLN GGAGAAGCATACT SEQ ID  Primeranti-sense TGTAGAAG NO: 12 RFC4 Probe RFC4 CAGCGATTACTAG SEQ ID  ProbeACATTGCCAAGAA NO: 13 RFC4 Primer RFC4 CAAGCCTCTGTCA SEQ ID  Primer senseGATAA NO: 14 RFC4 Primer RFC4 CCACCTGTTAATC SEQ ID  Primer anti-senseGAGTA NO: 15

Levels of nucleic acid gene expression products can be determined bymeasuring and/or testing the amount of, e.g., the reference nucleic acidsequences listed in Table A, as well as complements, fragments, andsimilar nucleic acid sequences of the reference nucleic acid sequenceslisted in Table A. “Similar nucleic acid sequences” can be naturallyoccurring (e.g., allelic variants or homologous sequences from otherspecies) or engineered variants relative to the reference nucleic acidsequences in Table A and will be at least about 60, 65, 70, 75, 80, 85,90, 95, 96, 97, 98, 99% or more identical (or hybridize under highlystringent hybridization conditions to a complement of a nucleic acidsequence listed in Table A) over a length of at least about 10, 20, 40,60, 80, 100, 150, 200 or more nucleotides or over the entire length ofthe reference nucleic acid sequences in Table A. Fragments of thereference nucleic acid sequences in Table A—or similar nucleic acidsequences—can be of any length sufficient to distinguish the fragmentfrom other sequences expected to be present in a mixture, e.g., at least5, 10, 15, 20, 40, 60, 80, 100, 150, 200 or more nucleotides or at leastabout 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95% of the length of thereference nucleic acid sequences in Table A.

In particular embodiments, the expression levels of one or more of thegenes in Table A are measured or tested simultaneously. In someembodiments, microarrays (e.g., AFFYMETRIX®, AGILENT® andILLUMINA®-style arrays) can be adapted for use in the methods providedby the invention. In other more particular embodiments, microarrays arenot used. In other embodiments, techniques or assays with a sensitivityof only 50,000 or more myeloma cells/sample are not used.

In another related aspect, the invention provides oligonucleotideprimers that are suitable for detecting (e.g., measuring and/or testing)the expression level of genes in Table A for prognosing a subject with,or suspected of having, multiple myeloma. Sets of oligonucleotideprimers may be prepared for any of the combinations of genes in Table Adescribed in the application. The oligonucleotide primers provided bythe invention can readily be designed using ordinary skill in the art ofmolecular biology to arrive at primers that are specific for a givengene in Table A (as well as fragments and similar nucleic acid sequencesthereof, as described above)—i.e., so that the primers can discriminatethe target nucleic acid from other nucleic acids present (or expected tobe present) in a sample, including entire transcriptomes and/or primersdirected to other genes in Table A. The length (e.g., about 10-100nucleotides, e.g., about 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, 100 nucleotides,or more) and sequence (i.e., the particular portion of a gene in Table Ato which the primer hybridizes) of the primers can readily be adjustedto achieve a desired melting temperature (“Tm”; e.g., about 45-72° C.,e.g., about 45, 50, 55, 60, 65, 70, 72° C. or more) and specificity. Theskilled artisan will readily account for factors such as secondarystructures, primer dimers, salt concentrations, nucleic acidconcentrations, et cetera. Oligonucleotide primers provided by theinvention may consist of (or consist essentially of) naturally occurringdeoxribonucleotides or, optionally, may include modifications such asnon-natural nucleotides, artificial backbones (such as PNAs), anddetectable labels, such as florescent labels, biotinylation, et cetera.

Protein levels for genes listed in Table A, including any of theparticular combinations described throughout the application, can bemeasured or tested by quantitative cytochemisty or histochemisty, ELISA(including direct, indirect, sandwich, competitive, multiple andportable ELISAs (see, e.g., U.S. Pat. No. 7,510,687)), western blotting(including one, two or higher dimensional blotting or otherchromatographic means—optionally including peptide sequencing), RIA(radioimmunoassay), SPR (surface plasmon resonance), nucleic acid-basedor protein-based aptamer techniques, HPLC (high precision liquidchromatography), peptide sequencing (such as Edman degradationsequencing or mass spectrometry (such as MS/MS), optionally coupled toHPLC), and microarray adaptations of any of the foregoing (includingnucleic acid, antibody or protein-protein (i.e., non-antibody) arrays).

Protein techniques typically, but not necessarily, employ antibodies (incontrast to, for example, direct sequencing). Antibodies for use in themethods provided by the invention can be directed to a peptide sequencecorresponding to any of the genes listed in Table A, as well asfragments of these sequences, similar peptide sequences, and fragmentsof similar peptide sequences. “Similar peptide sequences” can benaturally occurring (e.g., allelic variants or homologous sequences fromother species) or engineered variants to the genes in Table A and willexhibit substantially the same biological function and/or will be atleast about 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99% or morehomologous (i.e., conservative substitutions (see, e.g., Heinkoff andHeinkoff, PNAS 89(22):10915-10919 (1992) and Styczynski et al., Nat.Biotech. 26(3):274-275 (BLOSUM, e.g., BLOSUM 45, 62 or 80) or Dayhoff etal., Atlas of Protein Sequence and Structure (Volume 5, Supplement 3),Nat. Biomed. Res. Found., pp. 345-358 (PAM, e.g., PAM 30 or 70)) oridentical at the amino acid level over a length of at least about 10,20, 40, 60, 80, 100, 150, 200 or more amino acids or over the entirelength of a protein product of the genes in Table A. Fragments ofprotein products of the genes in Table A—or similar peptidesequences—can be of any length sufficient to distinguish the fragmentfrom other sequences expected to be present in a mixture, e.g., at least5, 10, 20, 40, 60, 80, 100, 150, 200 or more amino acids or at leastabout 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 95% of the length ofprotein products of the genes in Table A.

The term “antibody,” as used herein, refers to an immunoglobulin or apart thereof, and encompasses any polypeptide comprising an antigenbinding site regardless of the source, species of origin, method ofproduction, and characteristics. As a nonlimiting example, the term“antibody” includes human, orangutan, mouse, rat, goat, rabbit, sheep,and chicken antibodies. The term includes, but is not limited to,polyclonal, monoclonal, monospecific, polyspecific, humanized, fullyhuman, camelized, single chain, chimeric, synthetic, recombinant,hybrid, mutated, and CDR-grafted antibodies. For the purposes of thepresent invention, it also includes, unless otherwise stated, antibodyfragments such as Fab, F(ab′)2, Fv, scFv, Fd, dAb, VHH (also referred toas nanobodies), and other antibody fragments that retain the antigenbinding function. Antibodies also include antigen-binding molecules thatare not based on immunoglobulins, as further described below.

For example, in some embodiments, the term “antibody” includes anantigen-binding molecule based on a scaffold other than animmunoglobulin. For example, non-immunoglobulin scaffolds known in theart include small modular immunopharmaceuticals (see, e.g., U.S. PatentApplication Publication Nos. 2008/0181892 and 2008/0227958, publishedJul. 31, 2008, and Sep. 18, 2008, respectively), tetranectins,fibronectin domains (e.g., ADNECTINS®; see U.S. Patent ApplicationPublication No. 2007/0082365, published Apr. 12, 2007), protein A,lipocalins (see, e.g., U.S. Pat. No. 7,118,915), ankyrin repeats, andthioredoxin. Molecules based on non-immunoglobulin scaffolds aregenerally produced by in vitro selection of libraries by phage display(see, e.g., Hoogenboom, Method Mol. Biol. 178:1-37 (2002)), ribosomedisplay (see, e.g., Hanes et al., FEBS Lett. 450:105-110 (1999) and Heand Taussig, J. Immunol. Methods 297:73-82 (2005)), or other techniquesknown in the art (see also Binz et al., Nat. Biotech. 23:1257-1268(2005); Rothe et al., FASEB J. 20:1599-1610 (2006); and U.S. Pat. Nos.7,270,950; 6,518,018; and 6,281,344) to identify high-affinity bindingsequences.

To perform the methods provided by the invention, the invention furtherprovides kits comprising reagents for performing any of the methodsprovided by the invention. Typically, the kits provided by the inventioncomprise (or consist essentially of or consist of) reagents fordetecting, measuring and/or testing the expression level of two or moregenes in Table A, i.e., at least 2, 3, 4, or all 5 genes. The kits mayinclude, for example, oligonucleotide primers provided by the invention,antibodies provided by the invention, or a combination thereof; incertain embodiments, these reagents are artificially and detectablylabeled. Kits will typically include instructions for use. Optionally,the kits may include “suitable positive controls,” which arecompositions comprising (or consisting essentially of or consisting of)nucleic acids, proteins, or nucleic acids and proteins that exhibit anabnormal expression pattern of genes from Table A. For example, suitablecontrols may be from a clinical source known to have favorable orunfavorable multiple myeloma prognosis and may include either fixed orpreserved but otherwise unprocessed biopsy tissue, or, alternatively,isolated fractions from such biopsies, including fractions comprising(consisting of or consisting essentially of) nucleic acids (e.g., mRNAor cDNAs thereof), protein, and combinations thereof (e.g., at least 20,40, 50, 60, 70, 80, 90, 95, 97, 99% by dry weight, or more, nucleic acidand/or protein). Alternatively, in certain embodiments, the suitablepositive controls may comprise artificial mixtures of nucleic acidsand/or proteins, e.g., combined in proportions characteristic of anabnormal expression pattern of 2 or more genes from Table A.

Analysis

Gene expression levels can be analyzed by any means in the art. Beforefurther analysis, raw gene expression data can be transformed, e.g.,log-normalized, expressed as an expression ratio, percentile-ranked, orquantile-scaled, etc. Data may further be modified by any nonparametricdata scaling approach.

Expression patterns can be evaluated and classified by a variety ofmeans, such as general linear model (GLM), ANOVA, regression (includinglogistic regression), support vector machines (SVM), linear discriminantanalysis (LDA), principal component analysis (PCA), k-nearest neighbor(kNN), neural network (NN), nearest mean/centroid (NM), and baysiancovariate predictor (BCP). A model, such as SVM, can be developed usingany of the subsets and combinations of genes described herein based onthe teachings of the invention. In more particular embodiments, anexpression pattern is evaluated as the mean of log-normalized expressionlevels of the genes.

A selected threshold for an expression profile (summarized as a riskscore) can be set to achieve a desired sensitivity or specificity,and/or to stratify subjects based on a relative hazard ratio betweenstratification groups. For example, in some embodiments, a disease indexthreshold is set to achieve a “hazard ratio” (ratio of overall survival,event-free survival, or progression free survival for multiple myelomasubjects between two stratification groups, e.g., high and low riskprognosis groups) of about 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.2, 2.4, 2.6,2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 5.2, 5.4,5.6, 5.8, 6.0, or more over a period of, e.g., 6, 12, 18, or 24 months,or 3, 4, 5, 6, 7, 8, 9, or 10 years. “Stratification groups” are themembers of a data set satisfying one or more stratification criteria—forexample, a percentile rank of disease index, such as all group memberswith a disease index greater than or equal to about the 60^(th)percentile or a mean log-normalized gene expression profile of less thanabout 5 or between about 5 and about 15, e.g., greater than about 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, or more, such as greater than about 8,9, or 10, more particularly, greater than 10, e.g., greater than about10.68. Stratification groups may be compared by any means by anystatistic, such as mean, median, mode, and/or standard deviation of anyclinical parameter, such as age, duration of disease, OS, PFS, etcetera.

Treatment Methods

In another aspect, the invention provides methods of treating multiplemyeloma. For example, any of the methods provide by the invention can,in certain embodiments, further comprise the step of providing (e.g.,administering, prescribing, or otherwise making available) a suitablemyeloma therapy to the subject, e.g., one based on the subject'sprognosis by the methods provided by the invention. In other relatedembodiments, the results of the prognostic methods provided by theinvention provide a new indication for providing (e.g., administering,prescribing, or otherwise making available) or modifying a myelomatherapy to a subject and/or changing the surveillance schedule for asubject. For example, in some embodiments, a poor prognosis (high riskmyeloma) by the methods provided by the invention may indicate the needto provide a more aggressive myeloma therapy (either by adding one ormore treatments to a regimen and/or increasing the dose of one or moretreatments), or, in some embodiments, a less aggressive treatment,focusing on patient comfort instead of eliminating disease, may beappropriate. In some embodiments, a favorable prognosis (low riskmyeloma) can, in some embodiments, indicate that a more aggressivemyeloma therapy can be avoided, withdrawn, or modified (e.g., removingone or more treatments from a regimen and/or decreasing the dose of oneor more treatments), or in some embodiments, more aggressive therapy maybe used to attempt to eliminate disease. In other embodiments, followingprognosis by the methods provided by the invention, the surveillanceschedule for a subject is changed—e.g., subjects prognosed with highrisk myeloma may receive a more frequent surveillance schedule, whilesubjects prognosed with low risk myeloma may receive a less frequentsurveillance schedule. Surveillance methods for subjects with multiplemyeloma are well known in the art and include, for example,cytogenetics, PET (positron emission tomography) scans, MM (magneticresonance imaging), DWIP, bone marrow biopsy, serum or urineelectrophoresis (including monitoring one or more of β2 microglobulinlevels, M protein levels (gamma spike), and immunoglobulin levels(including whole Ig, heavy chain, light chain)), and any combination ofthe foregoing.

“Myeloma therapy” includes both established and experimental treatmentsfor multiple myeloma in humans. In certain embodiments, myeloma therapycomprises treatment with a therapeutically effective amount of one ormore agents selected from a proteasome inhibitor, an immunomodulatorydrug, cisplatin (see, e.g., PubChem 84691, 441203), etoposide (see,e.g., PubChem 36462), cyclophosphamide (see, e.g., PubChem 2907),melphalan (see, e.g., PubChem 460612), cellular therapy with expanded NKcells, cellular therapy with T-cells, antibody therapy, dexamethasone(see, e.g., PubChem 5743), and combinations thereof (e.g., 1, 2, 3, 4,5, 6, or more of any of the foregoing—termed “combination myelomatherapy”).

Exemplary proteasome inhibitor myeloma therapies include one or more ofbortezomib (see, e.g., PubChem 387447), carfilzomib (see, e.g., PubChem11556711), disulfiram (see, e.g., PubChem 3117),epigallocatechin-3-gallate (see, e.g., PubChem 65064), salinosporamide A(see, e.g., PubChem 11347535), epoxomicin (see, e.g., PubChem 16760412),MG132 (see, e.g., PubChem 462382), ONX 0912 (see, e.g., PubChem25067547), CEP-18770 (see, e.g., PubChem 24800541), MLN9708 (see, e.g.,PubChem 49867936), and combinations thereof. In more particularembodiments, bortezomib and/or barfilzomib are proteasome inhibitors foruse in a myeloma therapy.

Exemplary immunemodulary myeloma therapies include, e.g., thalidomide(see, e.g., PubChem 5426; in particular embodiments, an S-racemate maybe used), lenalidomide (see, e.g., PubChem 216326), pomalidomide (see,e.g., PubChem 134780), apremilast (see, e.g., PubChem 11561674), andcombinations thereof.

Antibody myeloma therapies include antibodies (as defined above),including, in some embodiments, neutralizing antibodies or antibodieswith ADCC activity, that specifically bind to CD20 (human GeneID No.931), SLAMF7 (SLAM family member 7, human GeneID No. 57823 (otheraliases: CD319, CS1)), CD38 (human GeneID No. 952), or CD138 (humanGeneID No. 6382), and as well as combinations of these antibodies.

Particular exemplary combination myeloma therapies include the treatmentregimes termed total therapy (TT)2, TT3a, TT3b, TT4, TT5, or TT6. Otherexemplary combination myeloma therapies include thalidomide withdexamethasone, optionally further including melphalan.

EXEMPLIFICATION

Introduction

The prognostic value of gene expression profiling (GEP) in multiplemyeloma (MM) has been reported by several groups. The GEP70 model (Blood109(6):2276-2284 (2007)) was developed in Total Therapy 2 (TT2), and itsdiscriminatory power for progression-free survival (PFS) and overallsurvival (OS) has been validated in several published datasets in thetransplant, non-transplant and relapse settings. See Journal of ClinicalOncology 26(29):4798-4805 (2008); Blood 115(21):4168-4173 (2010); Blood109(8):3177-3188 (2007); Blood 111(2):968-969 (2008); and Leukemia22(2):459-461 (2008). We applied the GEP70 model to Total Therapy 6(TT6), a tandem transplant trial for previously treated MM notqualifying for front-line protocols (TT3, TT4 or TT5). The 24-moestimates of OS were 95% and 18% for low-risk and high-risk MM,respectively.

Methods

Patients

Our training data consist of 56 MM patients on TT6 and 275 on TT3a withboth survival and GEP data available. An additional 166 patients on TT3band 351 patients on TT2 were used for validation. The details of the TT2and TT3 have been previously published elsewhere. See Blood115(21):4168-4173 (2010); The New England Journal of Medicine354(10):1021-1030 (2006); Journal of Clinical Oncology 28(7):1209-1214(2010); and Blood 116(8):1220-1227 (2010). Details of TT6 are reportedin Supplemental Methods, below.

Gene Expression Profiling

GEP sample procurement and processing as well as calculations of theGEP70 risk score have been reported previously. See Blood109(6):2276-2284 (2007).

Survival Estimation

PFS and OS durations were measured from start of protocol therapy;progressions included relapse or death from any cause in the former anddeath from any cause in the latter. OS and PFS curves were estimatedwith the Kaplan-Meier method (Journal of the American StatisticalAssociation (53):457-481 (1958)) and compared by the log-rank test(Journal of the Royal Statistical Society 135(2):185-207 (172)). Hazardratios were estimated with Cox regression models (Journal of the RoyalStatistical Society Series B(34):187-220 (1972)) and stepwise Coxregression analysis was conducted to select an optimal multivariatemodel as well as provide evidence for independent prognostic power of arisk score.

Results and Discussion

We ranked all 70 probe sets included in the GEP70 risk model by their pvalues, based on univariate Cox regression analysis for OS in TT6 (Table2). The top n probe sets with the smallest p values were combined tocreate a continuous score using similar methodology as in the GEP70model development.

To identify a high-risk patient group, we employed the running log ranktest to determine an optimal cutoff for the new risk score so thatpatients with scores higher than the cutoff were deemed high-risk andotherwise low-risk. We found that a model based on as few as two genesreliably predicted risk for patients with MM on TT6 (FIG. 3). Next, weselected the top five probe sets (corresponding to the genes ENO1,FABP5, TRIP13, TAGLN2, and RFC4) to form a 5-probe score (referred to asGEP5 hereafter; FIG. 1A). Since each of the five probe sets had apositive association with death rate in TT6, the GEP5 score was simplythe mean of log 2 transformed expression levels of the five probe sets.Since the number of patients treated on TT6 was relatively small with arelatively short follow up, we next sought to establish the GEP5 modelin a larger dataset of uniformly treated patients with a longer followup data. Furthermore, we wanted to see whether the GEP5 was alsoapplicable to previously untreated patients. We therefore established anoptimal cutoff by the running log rank test for the GEP5 score in TT3a.In TT3a, the GEP5 score shows significant differences between high- andlow-risk disease for OS and PFS which are comparable to those obtainedby the GEP70 risk model using its established cutoff of 0.66 (FIG. 1Band FIG. 2B). We validated the new cutoff in TT3b, where we alsoobserved a striking similarity between the results obtained using GEP5and GEP70 (FIG. 1C and FIG. 2C). On multivariate analysis, theGEP5-defined high risk was selected as an independent adverse variablelinked to inferior PFS with an estimated hazard ratio of 3.29 (95% CI:1.92-5.64) but was not selected as significant for OS. The GEP70 model,in contrast, was selected for OS but not PFS (Table 3). In TT2, wherethe GEP70 model was developed, the GEP5- and GEP70-defined low riskgroups again demonstrated highly similar clinical outcomes, although thehigh-risk group defined by the GEP5 model appears to have highersurvival estimates than by the GEP70 model (5 year estimated OS 40% vs.28%, 5-year estimated PFS 26% vs 15%) (FIG. 1D). This is also seen whenthe TT2 data are separated by treatment arm (FIGS. 4A-4D). Table 1 givesa summary of the univariate survival analysis on the GEP5 and GEP70models. Cross-tabulation of GEP70 and GEP5 risk for TT2, TT3a and TT3bshowed an agreement rate of 0.90, 0.89 and 0.87, respectively (Table 3).

ENO1 encodes alpha-enolase, one of three enolase isoenzymes found inmammals. Alternative splicing of this gene results in a shorter isoformthat produces a protein, MYC Binding Protein 1, which acts as atranscriptional repressor and possibly as a tumor suppressor. ENO1 isinduced in diffuse large cell lymphoma (DLCL) after treatment with thenatural biological agent Bryostatin1 and is up-regulated in response toenterovirus-71 (EV71) infection (at protein level). FABP5 is a member ofthe fatty acid binding proteins family. Over-expression of FABP5 hasbeen associated with poor survival in triple negative breast cancer andwith resistance to ATRA in a preclinical model of pancreatic ductaladenocarcinoma. TRIP13 encodes a protein that interacts with the ligandbinding domain of thyroid hormone receptors, also known ashormone-dependent transcription factors. It has been suggested to play arole in in early-stage non-small cell lung cancer. TAGLN has beenreported as a tumor suppressor and under-expression was associated withpoor prognosis in colorectal and prostate cancer, whereas it was foundto be over-expressed in senescent human fibroblasts. RFC4 encodes the 37kD subunit of the replication factor C (RFC) protein complex. RFC andthe proliferating cell nuclear antigen (PCNA) are required for DNAelongation and thus proliferation of cells.

Recently, a large-scale proteomics experiment involving 85 patients withmyeloma identified ENO1, FABP5 and TAGLN among a set of 24 proteins thatare associated with short OS. This set of 85 patients included 47patients who were treated on TT3b. This correlation between mRNA andprotein expression supports the biologic relevance of the GEP5 model.

Supplemental Methods

Total Therapy 6 is designed as an open-label phase 2 protocol forpatients with symptomatic multiple myeloma (MM) with at least one priorline of chemotherapy. Patients are treated as follows:

1) INDUCTION: MEL-10 (10 mg/m²) VTD-PACE and peripheral blood stem cellcollection

2) 1^(st) TRANSPLANT: MEL-80 (20 mg/m²/day×4 days)+VRD-PACE

3) INTERIM THERAPY: MEL-20 (5 mg/m²/day×4 days)+VTD-PACE (75%) 2 cycles

4) 2^(nd) TRANSPLANT: MEL-80 (20 mg/m²/day×4 days)+VRD-PACE

5) MAINTENANCE: VRD alternating with: VMD

6) Q 1 month in Year 1

7) Q 2 months in Years 2 and 3.

MEL: melphalan; VTD-PACE: bortezomib (Velcade), thalidomide,dexamethasone, cisplatin, doxorubicin, cyclophosphamide, etoposide;VRD-PACE: bortezomib (Velcade), lenalidomide (Revlimid), dexamethasone,cisplatin, doxorubicin, cyclophosphamide, etoposide; VRD: bortezomib(Velcade), lenalidomide (Revlimid), dexamethasone; VMD: bortezomib(Velcade), melphalan, dexamethasone. This protocol was approved by anInstitutional Review Board.

TABLE 1 Summary of the GEP70 and GEP5 models by univariate P values whenconsidered as binary as well as continuous variables Outcome Gene P incontinuous P in binary Protocol Variable Predictor Cox analysis Logrankanalysis TT2 OS GEP5 4.23E−08 7.05E−05 GEP70 1.99E−14 2.85E−10 PFS GEP50.000514 0.000783 GEP70 8.24E−10 9.84E−08 TT3a OS GEP5 1.76E−10 3.87E−05GEP70 1.65E−11 2.98E−10 PFS GEP5 1.14E−05 0.001044 GEP70 7.75E−102.75E−08 TT3b OS GEP5 4.17E−10 5.67E−06 GEP70 2.11E−08 1.09E−06 PFS GEP51.72E−10 3.35E−07 GEP70 3.50E−08 1.85E−05

TABLE 2 Hazard ratio and p value regarding OS in TT6 for the top 20 eachof the 70 probe sets in GEP70 model HR Probe Gene Chromosome Original(95% Set Symbol Location HR >=1 CI) P Q Rank 201231_s_at ENO1chr1p36.3-p36.2 1 4.11 0.000017 0.000049 1 (2.16, 7.82) 202345_s_atFABP5 chr11q12.1 /// 1 2.74 0.000018 0.000049 2 chr13q14.3 /// (1.73,chr8q21.13 4.33) 204033_at TRIP13 chr5p15.33 1 5.14 0.000077 0.000119 3(2.28, 11.56) 200916_at TAGLN2 chr1q21-q25 1 2.16 0.000086 0.000119 4(1.47, 3.18) 204023_at RFC4 chr3q27 1 5.44 0.000150 0.000149 5 (2.27,13.06) 202729_s_at LTBP1 chr2p22-p21 0 0.33 0.000195 0.000149 6 (0.18,0.59) 200750_s_at RAN chr12q24.3 1 10.15 0.000207 0.000149 7 (2.98,34.50) 225582_at KIAA1754 chr10q25.1 0 0.26 0.000217 0.000149 8 (0.13,0.53) 203432_at TMPO chr12q22 1 4.87 0.000253 0.000155 9 (2.09, 11.39)225834_at MGC57827 chr1p12 /// 1 2.48 0.000371 0.000204 10 chr1q32.1(1.51, 4.10) 226936_at C6orf173 chr6q22.32 1 3.60 0.000519 0.000251 11(1.75, 7.43) 213628_at MCLC chr1p13.3 0 0.24 0.000548 0.000251 12 (0.11,0.54) 204092_s_at STK6 chr20q13.2-q13.3 1 3.64 0.000716 0.000295 13(1.72, 7.70) 200966_x_at ALDOA chr16q22-q24 1 4.15 0.000751 0.000295 14(1.81, 9.48) 1555864_s_at PDHA1 chrXp22.2-p22.1 1 16.88 0.0010700.000367 15 (3.10, 91.81) 227547_at LOC388795 — 0 0.14 0.001104 0.00036716 (0.04, 0.45) 206364_at KIF14 chr1q32.1 1 2.04 0.001133 0.000367 17(1.33, 3.14) 210334_x_at BIRC5 chr17q25 1 3.33 0.001199 0.000367 18(1.61, 6.89) 201947_s_at CCT2 chr12q15 1 7.01 0.001355 0.000393 19(2.13, 23.05) 201897_s_at CKS1B chr1q21.2 1 3.69 0.001531 0.000421 20(1.65, 8.26)

In Table 2, the rows are ranked by p value from smallest to largest;false discovery rate was estimated by the Q value method. The column“Original HR>=1” indicates whether the hazard ratio of a probe setwas >=1 in the original TT2 training set for GEP70 model development(1=yes; 0=no). The top five probe sets were used to generate the GEP5model.

TABLE 3 Multivariate stepwise Cox regression analysis on TT3b test setOverall Survival Progression-free Survival Variable n/N (%) HR (95% CI)P-value HR (95% CI) P-value Multivariate White 148/159 (93%)  0.42(0.18, 1.00) 0.049 0.38 (0.18, 0.82) 0.014 GEP70 High Risk 36/159 (23%)4.43 (2.46, 8.00) <.001 B2M >5.5 mg/L 48/159 (30%) 1.76 (1.02, 3.02)0.042 GEP5 high risk 42/159 (26%) 3.29 (1.92, 5.64) <.001 MultivariateFemale 61/159 (38%) 2.10 (1.16, 3.81) 0.014 (without Albumin <3.5 g/dL71/159 (45%) 2.54 (1.34, 4.81) 0.004 GEP70) GEP5 high risk 42/159 (26%)2.99 (1.63, 5.46) <.001 3.29 (1.92, 5.64) <.001 White 148/159 (93%) 0.38 (0.18, 0.82) 0.014 B2M >5.5 mg/L 48/159 (30%) 1.76 (1.02, 3.02)0.042 Multivariate White 148/159 (93%)  0.42 (0.18, 1.00) 0.049 0.37(0.17, 0.79) 0.010 (without GEP5) GEP70 High Risk 36/159 (23%) 4.43(2.46, 8.00) <.001 3.25 (1.91, 5.54) <.001 Variable considered inunivariate analysis were: Age >=65 yr, Female gender, Caucasian race,Albumin <3.5 g/dL, B2M >= 3.5 mg/L, B2M >5.5 mg/L, Creatinine >= 2mg/dL, CRP >= 8 mg L, Hb <10 g/dL, LDH >= 190 U/L, Platelet Count <150 ×10{circumflex over ( )}9/L, Cytogenetic abnormalities, GEP70 High Risk,GEP proliferation index >= 10, GEP CD-1 subgroup, GEP CD-2 subgroup, GEPHY subgroup, GEP LB subgroup, GEP MF subgroup, GEP MS subgroup. GEP PRsubgroup, TP53 deletion, GEP5 high risk HR—Hazard Ratio, 95% CI—95%Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2 - Multivariate results not statistically significant at 0.05 level.All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 andvariable remains if meets the 0.05 level. A multivariate p-value greaterthan 0.05 indicates variable forced into model with significantvariables chosen using stepwise selection.

TABLE 4 Cross-tabulation of GEP70 and GEP5 high/low risk GEP5 ProtocolLow Risk High Risk Agreement Rate TT2 GEP70 Low Risk 286 19 0.90 HighRisk 15 31 TT3a GEP70 Low Risk 218 17 0.89 High Risk 12 28 TT3b GEP70Low Risk 115 14 0.87 High Risk 7 30

It should be understood that for all numerical bounds describing someparameter in this application, such as “about,” “at least,” “less than,”and “more than,” the description also necessarily encompasses any rangebounded by the recited values. Accordingly, for example, the description“at least 1, 2, 3, 4, or 5” also describes, inter alia, the ranges 1-2,1-3, 1-4, 1-5, 2-3, 2-4, 2-5, 3-4, 3-5, and 4-5, et cetera.

For all patents, applications, and other references cited herein, suchas non-patent literature and reference sequence or chemical information,it should be understood that they are incorporated by reference in theirentirety for all purposes as well as for the proposition that isrecited. Where any conflict exits between a document incorporated byreference and the present application, this application will control.

Headings used in this application are for convenience only and do notaffect the interpretation of this application.

The described computer-readable implementations may be implemented insoftware, hardware, or a combination of hardware and software. Examplesof hardware include computing or processing systems, such as personalcomputers, servers, laptops, mainframes, and micro-processors. Any ofthe computer-readable implementations provided by the invention may,optionally, comprise a step of providing a visual output to a user, suchas a visual representation on a screen or a physical printout.

Preferred features of each of the aspects provided by the invention areapplicable to all of the other aspects of the invention mutatis mutandisand, without limitation, are exemplified by the dependent claims andalso encompass combinations and permutations of individual features(e.g., elements, including numerical ranges and exemplary embodiments)of particular embodiments and aspects of the invention, including theworking examples. For example, particular experimental parametersexemplified in the working examples can be adapted for use in theclaimed invention piecemeal without departing from the invention. Forexample, for material that are disclosed, while specific reference ofeach various individual and collective combinations and permutation ofthese compounds may not be explicitly disclosed, each is specificallycontemplated and described herein, as are methods of making and usingsuch compounds. Thus, if a class of elements A, B, and C are disclosedas well as a class of elements D, E, and F and an example of acombination of elements A-D is disclosed, then even if each is notindividually recited, each is individually and collectivelycontemplated. Thus, in this example, each of the combinations A-E, A-F,B-D, B-E, B-F, C-D, C-E, and C-F are specifically contemplated andshould be considered disclosed from disclosure of A, B, and C; D, E, andF; and the example combination A-D. Likewise, any subset or combinationof these is also specifically contemplated and disclosed. Thus, forexample, the sub-groups of A-E, B-F, and C-E are specificallycontemplated and should be considered disclosed from disclosure of A, B,and C; D, E, and F; and the example combination A-D. This conceptapplies to all aspects of this application, including elements of acomposition of matter and steps of method of making or using thecompositions.

The forgoing aspects of the invention, as recognized by the personhaving ordinary skill in the art following the teachings of thespecification, can be claimed in any combination or permutation to theextent that they are novel and non-obvious over the prior art. Thus, tothe extent an element is described in one or more references known tothe person having ordinary skill in the art, they may be excluded fromthe claimed invention by, inter alia, a negative proviso or disclaimerof the feature or combination of features.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A kit comprising a primer set suitable foramplification of a nucleic acid encoding each of enolase 1 (ENO1), fattyacid binding protein 5 (FABP5), thyroid hormone receptor interactor 13(TRIP13), transgelin 2 (TAGLN2), and replication factor C (activator 1)4 (RFC4) in a nucleic acid sample isolated from a biological sampleisolated from a subject, wherein each primer set comprises at least oneprimer or probe having a detectable label, and wherein each primer setcomprises a primer selected from among SEQ ID NOS: 2, 3, 5, 6, 8, 9, 11,12, 14, or 15 and/or a probe selected from among SEQ ID NOS: 1, 4, 7,10, and
 13. 2. The kit of claim 1, wherein the detectable label is afluorescent label.
 3. The kit of claim 1, wherein each primer setcomprises at least one primer or probe having a detectable label andsubject to polymerase-dependent hydrolysis in the presence of the targettemplate of the set of three primers.
 4. The kit of claim 1, whereinprimer set suitable for amplification of enolase 1 (ENOL) comprises aforward primer comprising SEQ ID NO:2, a reverse primer comprising SEQID NO:3, and a probe comprising SEQ ID NO:1.
 5. The kit of claim 1,wherein primer set suitable for amplification of a nucleic acid encodingfatty acid binding protein 5 (FABP5) comprises a forward primercomprising SEQ ID NO:5, a reverse primer comprising SEQ ID NO:6, and aprobe comprising SEQ ID NO:4.
 6. The kit of claim 1, wherein primer setsuitable for amplification of a nucleic acid encoding thyroid hormonereceptor interactor 13 (TRIP13) comprises a forward primer comprisingSEQ ID NO:8, a reverse primer comprising SEQ ID NO:9, and a probecomprising SEQ ID NO:7.
 7. The kit of claim 1, wherein primer setsuitable for amplification of a nucleic acid encoding transgelin 2(TAGLN2) comprises a forward primer comprising SEQ ID NO:11, a reverseprimer comprising SEQ ID NO:12, and a probe comprising SEQ ID NO:10. 8.The kit of claim 1, wherein primer set suitable for amplification of anucleic acid encoding replication factor C (activator 1) 4 (RFC4)comprises a forward primer comprising SEQ ID NO:14, a reverse primercomprising SEQ ID NO:15, and a probe comprising SEQ ID NO:13.
 9. The kitof claim 1, wherein kit comprises the primers of SEQ ID NOS: 2, 3, 5, 6,8, 9, 11, 12, 14, and 15 and the probes of SEQ ID NOS: 1, 4, 7, 10, and13.